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Dataset debt in biomedical language modeling

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Item Type:Conference or Workshop Item
Title:Dataset debt in biomedical language modeling
Creators Name:Fries, J.A. and Seelam, N. and Altay, G. and Weber, L. and Kang, M. and Datta, D. and Su, R. and Garda, S. and Wang, B. and Ott, S. and Samwald, M. and Kusa, W.
Abstract:Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP. However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP's significant dataset debt - the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale. To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets. We find that only 13% of datasets are available via programmatic access and 30% lack any documentation on licensing and permitted reuse. Our dataset catalog is available at: https://tinyurl.com/bigbio22.
Source:2022 Challenges and Perspectives in Creating Large Language Models, Proceedings of the Workshop
Title of Book:Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models
Publisher:Association for Computational Linguistics (ACL)
Page Range:137-145
Date:May 2022
Additional Information:ACL materials are Copyright © 1963–2023 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Official Publication:https://doi.org/10.18653/v1/2022.bigscience-1.10

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